concordex 2.0.0

The goal of concordex is to identify spatial homogeneous regions (SHRs) as defined in the recent manuscript, “Identification of spatial homogenous regions in tissues with concordex”. Briefly, SHRs are are domains that are homogeneous with respect to cell type composition. concordex relies on the the k-nearest-neighbor (kNN) graph to representing similarities between cells and uses common clustering algorithms to identify SHRs.

Installation

concordex can be installed via pip

pip install concordex

.... and from Github

pip install git+https://github.com/pachterlab/concordex.git

Usage

After installing, concordex can be run as follows:

import scanpy as sc 
from concordex.tools import calculate_concordex

ad = sc.datasets.pbmc68k_reduced()

# Compute concordex with discrete labels
calculate_concordex(ad, 'louvain', n_neighbors=10)

# Neighborhood consolidation information is stored in `adata.obsm`
ad.obsm['X_nbc'][:3]

# The column names are stored in `adata.uns`
ad.uns['nbc_params']['nbc_colnames']

Citation

If you’d like to use the concordex package in your research, please cite our recent bioRxiv preprint:

Jackson, K.; Booeshaghi, A. S.; Gálvez-Merchán, Á.; Moses, L.; Chari, T.; Kim, A.; Pachter, L. Identification of spatial homogeneous regions in tissues with concordex. bioRxiv (Cold Spring Harbor Laboratory) 2023. https://doi.org/10.1101/2023.06.28.546949.

@article {Jackson2023.06.28.546949, 
    author = {Jackson, Kayla C. and Booeshaghi, A. Sina and G{'a}lvez-Merch{'a}n, {'A}ngel and Moses, Lambda and Chari, Tara and Kim, Alexandra and Pachter, Lior}, 
    title = {Identification of spatial homogeneous regions in tissues with concordex}, 
    year = {2024}, 
    doi = {10.1101/2023.06.28.546949}, 
    publisher = {Cold Spring Harbor Laboratory}, 
    URL = {<https://www.biorxiv.org/content/early/2024/07/18/2023.06.28.546949>},
    journal = {bioRxiv} 
}

Maintainer

Kayla Jackson